DexFLEX: Contact-Aware Foundation Controller for Command-Guided Dexterity
cs.RO, cs.AI, cs.LG
Submitted: 2026-10-09
Updated: 2026-10-09
Code: https://github.com/kevinzakka/mink
Sources
- Shared Autonomy via Deep Reinforcement Learning
- Residual Policy Learning for Shared Autonomy
- To the Noise and Back: Diffusion for Shared Autonomy
- DexterityGen: Foundation Controller for Unprecedented Dexterity
- Rotating without Seeing: Towards In-hand Dexterity through Touch
- Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
- RT-1: Robotics Transformer for Real-World Control at Scale
- Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation
- OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
- Visuo-Tactile World Models
- VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs
- Learning Versatile Humanoid Manipulation with Touch Dreaming
- AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
- DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation
- SPIDER: Scalable Physics-Informed Dexterous Retargeting
- FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing
- ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training
- SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation
- 3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
- FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies